Field
The disclosed embodiments generally relate to techniques for monitoring the operational health of electrical power plants and associated critical assets in the transmission and distribution grids. More specifically, the disclosed embodiments relate to a bivariate technique for tuning sequential probability ratio test (SPRT) parameters to facilitate prognostic surveillance of non-Gaussian sensor data from power plants.
Related Art
Electrical generation plants, such as gas-fired or coal-fired power plants, nuclear plants and wind farms, include numerous components, such as pumps, turbines and transformers, which routinely degrade over time and fail. Degradation of these components can be costly. For example, a pump in a nuclear plant can weigh up to 30 tons and can be radioactive, which means that repairing such a pump can take many weeks.
To reduce such costs, it is advantageous to proactively monitor components in power plants and the distribution grid to detect degradation early on, which makes it possible to fix impending problems while they are small. This type of proactive surveillance operates by monitoring time-series data from sensors in power plant and grid components, wherein the time-series data includes various parameters, such as temperatures, vibrations, voltages and currents. This time-series data can be analyzed using a prognostic surveillance technique, such as SPRT, to detect subtle degradation modes at the earliest incipience of the degradation. (For example, see U.S. Pat. No. 5,987,399, entitled “Ultrasensitive Surveillance of Sensors and Processes,” by inventors Stephan W. Wegerich, et al., issued Nov. 16, 1999) The SPRT technique can be used effectively in many systems to detect the incipience of degradation.
However, to operate effectively, SPRT relies on the time-series signals having a stationary, Gaussian distribution. A random stochastic process whose statistical moments are independent of time is said to be stationary. Some of the time-series signals from components in power plants are always stationary, at least during un-degraded operation. However, the majority of time-series signals in power plants are non-stationary, and/or have non-Gaussian noise contamination, and hence can vary dynamically during routine operation. Hence, the conventional SPRT technique may be ineffective in monitoring such non-stationary time-series signals.
What is needed is a prognostic-surveillance technique that can be used to effectively monitor dynamic, non-stationary, non-Gaussian time-series signals from power plants.